Automation systems are used to control different processes and monitor different environmental and operational conditions in a facility. As automation systems have gained wider acceptance, the size and breadth of the environmental and operational conditions monitored has consistently grown. A conventional automation system can monitor and store thousands of conditions.
Because of the expansion in the size of automation systems, analyzing the information gathered by these systems has become difficult. In addition, the complexity of the interaction of different systems operating in facilities has increased the complexity of this analysis. Because of this added complexity, analytical models have been developed to assist in the analysis of the data stored in automation systems. However, these models require a professional, such as an engineer, to review the information stored in the automation system to determine analytical rules that can be applied to the automation system to streamline the operation of the systems in the building.
Many times, the amount of data stored in a building automation system makes the cost of reviewing the information in the automation system impractical. Further, because of the large amount of data to review, many analytical rules that may be implemented are not apparent to the person reviewing the data, and are never implemented. Accordingly, a need exists for a system that can simplify the selection of operational rules based on information provided by an automation system.